Epictetus
271f79610d
New skills: - blacknode-graph-workflow - multi-agent-workflow-execution - langgraph-agent-workflow - langgraph-multi-agent-router - three-tier-evaluation-pipeline Config: LLM pipeline uses LFM on llama.cpp (8080)
97 lines
3.8 KiB
Markdown
97 lines
3.8 KiB
Markdown
---
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name: langgraph-multi-agent-router
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version: 1.0.0
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description: Orchestrate a multi-agent workflow where specialized agents collaborate
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sequentially to gather information, structure it, and generate a final response
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inputs:
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- User query string (e.g., destination location)
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- BedrockModel configuration (model_id, temperature, top_p)
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- Pre-configured agents with specific system prompts and tool sets
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steps:
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- Researcher agent executes with system prompt to gather raw destination facts (places,
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history, accommodations, food, web pages) using BedrockModel and available tools
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(calculator, current_time)
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- Travel guide agent receives raw research output and structures it into labeled sections
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(Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights,
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Suggested Web Pages)
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- Writer agent receives the structured guide and synthesizes it into a professional
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client-facing response with clear formatting and emphasis on the suggested web pages
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outputs:
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- Raw research data (JSON string containing gathered facts)
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- Structured guide content (markdown-formatted travel guide with labeled sections)
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- Final client response (professional formatted response ready for delivery)
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tags: []
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metadata:
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source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-multi-agent-router
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Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response
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## Setup
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**Dependencies:**
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```text
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pip install langchain langgraph bedrock-model pydantic
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```
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**Setup steps:**
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1. Install langchain and langgraph packages
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1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
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1. Create three Agent instances with specific system prompts and tool sets
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1. Initialize LangGraph with the agent chain and run the workflow
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## Key Files
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- `agents/langchain_langgraph/00-basic-agent/agent.py`
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- `agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py`
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- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
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## Steps
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1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
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2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
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3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
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## Implementation Details
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```python
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Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts
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```
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```python
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Travel guide agent receives raw output and formats into 5 labeled sections
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```
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```python
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Writer agent takes structured guide and writes professional client response
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```
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## Inputs
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- User query string (e.g., destination location)
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- BedrockModel configuration (model_id, temperature, top_p)
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- Pre-configured agents with specific system prompts and tool sets
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## Outputs
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- Raw research data (JSON string containing gathered facts)
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- Structured guide content (markdown-formatted travel guide with labeled sections)
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- Final client response (professional formatted response ready for delivery)
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## Failure Modes
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- Researcher agent fails to gather sufficient data or returns incomplete results
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- Travel guide agent fails to structure information correctly or produces unreadable output
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- Writer agent fails to format the final response properly or loses key information from the guide
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## Source
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Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
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Confidence: 0.95
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